Composite nested streams

ABSTRACT

Reshaping of streams is provided to facilitate utilizing the streams without rapidly increasing memory requirements as the size of the stream increases. The streams can be pushed to alternative storage upon being reshaped, for example, such as to a persistent storage. If the streams lose structure, for example if a hierarchical stream is reshaped into a flat structure for storage in a database, structural information can be stored along with the streams and utilized to shape the stream to its original structure upon request for data, for example. Streams can be pulled from an exposing device or application, and portions of the stream can be transformed and stored according to a set of stop elements; the stop elements can be associated with functions that take action on the stream upon reaching a stop element, such as transforming and storing a portion thereof.

BACKGROUND

The evolution of computers and networking technologies from high-cost,low performance data processing systems to low cost, high-performancecommunication, problem solving, and entertainment systems has provided acost-effective and time saving means to lessen the burden of performingevery day tasks such as correspondence, bill paying, shopping, budgetinginformation and gathering, etc. For example, a computing systeminterfaced to the Internet, by way of wire or wireless technology, canprovide a user with a channel for nearly instantaneous access to awealth of information from a repository of web sites and servers locatedaround the world. Such a system, as well, allows a user to not onlygather information, but also to provide information to disparatesources. As such, online data storing, management, and accessing hasbecome increasingly popular.

Consumer data can be stored in a variety of formats offering access to aplurality of entities. Extensible markup language (XML) and other datastorage, access, and management technologies have emerged to provide ahierarchical structure to data; the hierarchical structure provides anintuitive format to the data to facilitate programmatic access thereto.Both hierarchical and relational storage have benefits and drawbacks.One drawback of XML is that XML data is typically stored in memory tofacilitate programmatic access, which can become burdensome if the XMLfile is too large to fit in memory and/or is streamed in from anexternal data source. Relational data, however, can be storedpersistently and queried when desired without utilizing a significantportion of volatile storage.

However, XML queries can be more efficient due to the storage involatile memory and XML can be friendlier in regard to providingprogrammatic access, but, as XML files become large, processing andstoring them in volatile memory can have a negative affect on systemresources. XML is desirable as a method for storing, accessing, andcommunicating data; thus allowing data in XML format, or other nestedhierarchical format, to be seamlessly and efficiently stored in apersistent storage, such as relational storage, while maintaining itshierarchical structure can satisfy the current deficiencies of thelanguage. Current systems aim to store XML code in the database itself,but this requires extra steps of indexing the data as it comes in andallowing queries on the index, which may or may not produce desiredresults. This can also damage the hierarchical order of the XMLdepending on how the document or stream is broken up and stored. Oneissue that has been heretofore unsolved is how to process a nestedstream, such as XML, into a differently shaped stream, such as arelational database stream, while staying true to the hierarchy andorder.

SUMMARY

The following presents a simplified summary in order to provide a basicunderstanding of some aspects described herein. This summary is not anextensive overview nor is intended to identify key/critical elements orto delineate the scope of the various aspects described herein. Its solepurpose is to present some concepts in a simplified form as a prelude tothe more detailed description that is presented later.

Reshaping of nested streams into other structures and formats isprovided to facilitate alternative storage thereof, in one embodiment.The stream can be in substantially any nested/hierarchical format (suchas extensible markup language (XML), for example) and can be output tosubstantially any format. The output format can be flat, for example,such that the nesting/hierarchy and order of the original stream can bepreserved using data relating to the flat structure. The input streamcan be consumed and transformed, upon consumption, to a disparateformat. The disparately formatted stream can be pushed to one or moreoutput streams to facilitate alternative storage of the stream, forexample.

In one embodiment, the input stream can be a nested stream, such as XMLand storage thereof to a relational database can be desired. This can beadvantageous as relational databases can typically offer persistentstorage, thus taking the stream out of volatile memory for conservationof memory. In this embodiment, the nested stream can be read andinterpreted for indication of one or more stop elements, consumption ofwhich can cause an action on the stream. The action can be atransformation of a portion of the stream to a disparate structure forstorage of the portion. In one embodiment, a specified portion can betransformed into a relational format utilizing primary (and/or foreign)keys to preserve the hierarchy of the stream. Additionally, the streamcan be read one full XML element at a time to facilitate asynchronousprocessing and storage thereof within the relational data store withoutsacrificing hierarchy and order of the data. In this regard, the datacan be restructured to its original form in the stream for processingaccess requests to the data.

To the accomplishment of the foregoing and related ends, certainillustrative aspects are described herein in connection with thefollowing description and the annexed drawings. These aspects areindicative of various ways which can be practiced, all of which areintended to be covered herein. Other advantages and novel features maybecome apparent from the following detailed description when consideredin conjunction with the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a block diagram of an exemplary system thattransforms a data stream into one or more disparate streams.

FIG. 2 illustrates a block diagram of an exemplary system that stores adata stream in a disparate format in a disparate data store.

FIG. 3 illustrates a block diagram of an exemplary system that stores adata stream in a disparate format and provides subsequent accessthereto.

FIG. 4 illustrates a block diagram of an exemplary composite streamtransformation component.

FIG. 5 illustrates a block diagram of an exemplary system thatrestructures a nested data stream for storage in a relational store.

FIG. 6 illustrates an exemplary flow chart for transforming inputstreams to a disparate format for output thereof.

FIG. 7 illustrates an exemplary flow chart for configuring a streamtransformation architecture.

FIG. 8 illustrates an exemplary flow chart for pulling the stream andresponding to requests for elements in the stream.

FIG. 9 is a schematic block diagram illustrating a suitable operatingenvironment.

FIG. 10 is a schematic block diagram of a sample-computing environment.

DETAILED DESCRIPTION

Processing of data streams into disparately structured data streams isprovided to facilitate alternative storage thereof. Such restructuringcan be seamless to an accessing entity such that access can be providedto the disparately structured data stream in the substantially the samemanner as for the original data stream. Additionally, requests foraccess can be made in substantially the same manner regardless of thestructure of the disparately structured stream. In this regard, thestream can be restructured and stored in another format (or multipleother formats) but accessed in the same manner. It is to be appreciatedthat the accessing entity can also request access to the data usingmethods related to the disparate structure of the data.

In one embodiment, the data stream can be read (such as by subscription)and/or pulled (such as by request/response) from a data source. Thestream can be transformed into one or more streams of disparatestructure and/or format; the disparately structured/formatted stream canbe pulled by or pushed to another data reader, for example. This can beuseful in a scenario where a data stream is large and/or increasing insize such that a finite amount of memory is challenged. In thisscenario, the data stream can be consumed and off-loaded into a diskstorage type of format. The component or application reading the stream,then, can have a finite amount of memory and can leverage one or moreother components/applications having virtually infinite storage, forinstance. In one example, an extensible markup language (XML)document/stream can be read and transformed into multiple tables androws in a relational database. The transformation can preserve the orderand layout of the XML while breaking the document into logicalstructures for storage in the database. As mentioned, a call to anapplication to retrieve the XML can produce the same result whether ornot the XML has been transformed into relational database tables/rows.

Various aspects of the subject disclosure are now described withreference to the annexed drawings, wherein like numerals refer to likeor corresponding elements throughout. It should be understood, however,that the drawings and detailed description relating thereto are notintended to limit the claimed subject matter to the particular formdisclosed. Rather, the intention is to cover all modifications,equivalents and alternatives falling within the spirit and scope of theclaimed subject matter.

Now turning to the figures, FIG. 1 illustrates a system 100 thatfacilitates transforming a data stream into one or more disparatelyformatted/structured data streams. A composite stream transformationcomponent 102 is provided that takes a data stream as input (e.g. bysubscription and/or request/response) and transforms the stream into oneor more disparately formatted or structured streams. In one embodiment,the data stream is provided as input into main memory of the compositestream transformation component 102. The composite stream transformationcomponent 102 can read the stream and transform the stream into one ormore disparately formatted structures for storage in an alternativemedium, for example. This can facilitate off-loading the stream tohandle memory constraints; in this regard, the data stream can be largeand the composite stream transformation component 102 can have a finitememory, for example. Off-loading the stream to other storage in adifferent format can allow the data to be retained having little effecton the composite stream transformation component 102 memory or acomponent/application implementing such functionality.

When transforming the data stream to another format and storing it in adisparate medium, the composite stream transformation component 102 cankeep the logical structure of data. Thus, when a request for the data isprocessed, the data can be obtained from the disparate storage mediumand transformed from the disparate format to the original format as ifthe alternative transformation and storage were not performed. In thisway, the process can be seamless to a requesting entity. This storagetransformation process, however, can allow large data streams normallyloaded into memory to be off-loaded for access thereof where the memorycannot efficiently handle the data stream. In addition, providing anoff-loading format with efficient querying capability can make thetransformation process more seamless, such as a relational database.

In one embodiment, the data stream can be an XML stream, for example.The stream can be a large file and/or an infinite stream (such as astock/news ticker, for instance). As the file increases in size, or asthe stream keeps pushing data out, the available memory of the compositestream transformation component 102 (or an application running thecomponent) can continually decrease. However, in some applications, afull load of the XML stream in memory is desired for subsequent dataaccess/retrieval. Thus, off-loading the stream to a more fixed storagemedium, such as a disk, can allow the stream to be continually consumedwithout dropping portions of the stream or pausing to archive the data.In one embodiment, the data can off-loaded to a relational database thusproviding efficient querying capabilities as well. This can make theprocess seamless to a user of the data, for example. In this embodiment,the data can be consumed by an application or user from the compositestream transformation component 102, which consumes the data from thedata stream. Access to the data can be provided regardless of whetherthe portion of the stream requested is in memory of the composite streamtransformation component 102 and/or in off-loaded storage.

It is to be appreciated that this is one embodiment of a use casepertaining to the subject matter described herein. Other formats can beused for both the incoming and outgoing data streams. Additionally, oneor more outgoing data streams can be an input data stream to anothercomposite stream transformation component 102. As mentioned above, thetransformation process can retain the structure of the data. In the XMLexample, the hierarchy and order of items in the file can be retainedwhen transforming the data to relational database tables and rows, forexample. Thus, requesting the data can produce the database stored datain substantially the same format as if the data was retrieved from theoriginal data stream. Additionally, requests for data can be in the formof XML and/or relational database query in the XML example provided, forinstance.

Referring to FIG. 2, a system 200 for transforming a data stream intoone or more data store entries is displayed. A composite streamtransformation component 102 is displayed that inputs a data stream,transforms the stream, or a portion thereof, and outputs thetransformation to a data stream store 202 in one or more output datastreams. In one embodiment, the data stream input into the compositestream transformation component 102 can be a constant stream or a largefile. The composite stream transformation component 102 can transformthe data stream into one or more output streams. The output streams canbe in the format of data stream store 202, for example. The outputstreams can be stored in the data stream store 202 and dropped from thememory of the composite stream transformation component 102. In thisregard, neither the stream as input nor the streams as output areretained in the composite stream transformation component 102.

The data stream input into the composite stream transformation component102 can come by way of subscription (e.g. notification events of newdata and/or callback behavior) and/or as response to a request (from thecomposite stream transformation component 102 or other component, forinstance). For example, the input can be a file (such as a data file toolarge for composite stream transformation component 102 memory) and/or aconstantly updating stream. It is to be appreciated that the subjectmatter described herein is operable with substantially any size or typeof file; rather large files can facilitate discussion of one usefulembodiment. Example input can be substantially any streaming data, suchas a news/stock ticker, a media file (such as audio and/or video),sensor data (such as for many automation architectures), medicalinformation (such as patient data scrolling in a hospital room, forexample), airspace flight data (such as positions and other metrics),and/or the like. To this end, the input data can also be substantiallyany data that can be taken as a function of time as well, such asfitness data, computer input data, simulation data, driving data,network traffic data, and/or the like, for example.

In one embodiment, only some of the data can be desired such that thecomposite stream transformation component 102 can retain the desireddata and discard extraneous data. It is to be appreciated that thecomposite stream transformation component 102 need not necessarilyactively discard the extraneous data, rather the stream can be read anddoes not need to be stored in the first place; in this regard theextraneous data can simply be ignored. In this embodiment, the compositestream transformation component 102 can stop the stream at points ofinterest and transform the portion until an identifier is reachedindicating the end of the point of interest. This process can continuewhile the stream is live (e.g. constantly updating and/or offeringconnection thereto, for example). In one embodiment, the points ofinterest can correspond to a single output stream, for example. In oneembodiment, the point of interest can trigger an event in the compositestream transformation component 102 to stop the stream and begintransformation, for example.

Additionally, the data stream being input can be nested and/or have ahierarchical structure such that points of interest can occur before onepoint of interest ends. To preserve the structure of the data stream,the composite stream transformation component 102 can transform thestream, as mentioned, to disparate output streams relating to the pointsof interest. In this regard, a hierarchical/nested structure can bemaintained when storing the stream in a flat structure. For example, astream can be opened for a point of interest, and another point ofinterest can appear, opening another stream for the second point ofinterest. The stream for the second point can close (e.g. by an endingindicator for the point of interest) causing the output stream relatedto the second point of interest to be completely transformed and closed.Information pertaining to the other open streams can also be written toidentify the second point of interest as related (a child, for example)to the stream still open. Subsequently, an ending indicator for thefirst stream can be reached causing the first stream to be completelytransformed and closed.

In one embodiment, the data stream input is hierarchical, such as an XMLstream or document, having a plurality of tags indicating starting andending of elements. The elements can be nested such to indicatehierarchy. The stream can be input into the composite streamtransformation component 102 (e.g. by pulling or pushing) and thestart/end tags can be consumed until a point of interest element isreached. Subsequently, a stream relating to the element can be opened,and data consumed corresponding to the element can be transformed to adisparate structure, such as a relational database table/row, and outputaccordingly. In one embodiment, the data stream store 202 can be arelational database having tables relating to points of interest androws comprising the related data, for example (the data stream store 202can be distributed as well). Thus, the composite stream transformationcomponent 102 can open a stream relating to a table corresponding to anelement or point of interest. If another point of interest is consumedbefore the first is closed (by an end tag in the XML example), a newstream can be opened corresponding to a table relating to the secondpoint of interest. When an ending element is encountered for the secondstream, the desired related data can be output to the stream (and henceas rows to the database table, for example). The output can include, forexample, data regarding the first point of interest to indicatehierarchy (e.g. that the second point of interest is a child of thefirst point of interest). Subsequently, an end tag for the first pointof interest can be reached causing the related stream to be written andclosed.

It is to be appreciated that error checking can be present in thetransformations performed by the composite stream transformationcomponent 102 as well, such that, for example, where an end tag for thefirst point of interest is reached before the end tag for the secondpoint of interest (indicating a nesting error), the second point ofinterest can be closed. Additionally, the output stream can be insubstantially any format or architecture where the format orarchitecture is different from the data stream as input (such asrelational, flat-file, tab-delimited file, comma separated value file,JavaScript object notation (JSON), representational state transfer(REST), XML, etc.). Additionally, as mentioned, elements can be ignored,and thus, not included in the output stream, for instance. Also,accessing mechanisms can be implemented to allow access to data storedin the output format from requests based on the input format. Moreover,the composite stream transformation component 102 can be configurableand/or utilized with an interface to provide customization thereof.Additionally, inference can be used to define transformations, at leastin part, based on previous uses, for example. In one embodiment of thesubject matter as described, the composite stream transformationcomponent 102 can comprise a reader for the data stream as well as areader for the streams output to the data stream store 202. The readerof the data stream can read an entire available segment of the stream,for example, and the reader for the output streams can traverse the readstream one element at a time. Thus, in the XML example, an availableportion of the stream can be read by the composite stream transformationcomponent 102, and then elements can be traversed one by one to createthe output streams for the data stream store 202.

Turning now to FIG. 3, a system 300 for storing a data input stream in astream data store and providing access thereto is shown. A compositestream transformation component 102 is provided that pulls or is pusheda data stream and outputs a transformed version of the stream to one ormore data stream store(s) 202. Additionally, an application 302 isprovided that requests access to a portion of the data in the datastream. In one embodiment, the composite stream transformation component102 receives a data stream as input and transforms the stream to one ormore disparate formats for storage thereof in data stream store(s) 202;the transformation can shred the stream for relevant portions thereof,for example. The application 302 can desire access to a portion of thestream. In one embodiment, the request for access can be in a formatcorresponding to a format of the original data stream. The compositestream transformation component 102 can interpret the request and accessthe data stream store(s) (such as by query, for example) to obtain thedesired data. It is to be appreciated that if the data is in thecomposite stream transformation component 102 (such as part of a pointof interest being transformed), the composite stream transformationcomponent 102 can return directly from the data stream as well.

For example, as mentioned, the data stream can be a news tickerimplemented in really simple syndication (RSS—an embodiment of XML). Asthe news ticker stream comes in (as the data stream input into thecomposite stream transformation component 102), the data can be consumedlooking for a point of interest, for instance. The points of interestcan be defined by the application 302. In this regard, there can be acomposite stream transformation component 102 specific to a givenapplication 302, for example. In addition, multiple composite streamtransformation components 102 can pull on (or receive subscriptionnotifications from) one data stream and output one or more streamsaccordingly, for example. In one embodiment, there can be someinformation in a stream the application 302 does not need to accessand/or the composite stream transformation component 102 deemsunnecessary, such as keywords, related news stories, or otherinformation that can be specific to one embodiment. Thus, the compositestream transformation component 102 can define stops in the stream tocorrespond to points of interest and specify portions of the stream tobe extracted at the points of interest. In this regard, consuming apoint of interest can trigger an event (such as a callback function, forexample), to process the data in the stream relating to the point ofinterest. In this example, the composite stream transformation component102 can detect the point of interest and extract relevant informationoutputting such to one or more data streams into one or more data streamstore(s) 202, for example. Additionally, as mentioned, the compositestream transformation component 102 can transform the stream such thatthe hierarchical structure is preserved. For example, RSS (and XML) canhave nested related elements, such to create a hierarchy of elements.The hierarchy can be preserved by storing relationship relatedinformation (such as parent, child, and/or sibling information) with apoint of interest when outputting to the data stream store(s) 202.

The application 302 can request a portion of the data stream as storedin the data stream store(s) 202, for example. The request can beprocessed by the composite stream transformation component 102, forinstance, by querying the data stream store(s) 202 for the requesteddata. In one embodiment, the resulting data can be transformed into thetype and/or format of the original stream to make the transformationseamless to outside applications and other requesters, for example. Inthis regard, the composite stream transformation component 102 can havefinite memory as streams can be discarded once output to the data streamstore(s) 202, for example. As mentioned, where the data has ahierarchical or nested format, this format can be preserved in the datastream store(s) 202. For example, where the store(s) 202 are relationaldatabases, primary keys can be defined in a table related to a point ofinterest and utilized by other tables related to children (or nested)points of interest (as a field thereof, for example) to keep therelationship hierarchy. In this regard, a query from the compositestream transformation component 102 to the data stream store(s) canrender one or more database rows in this example. The row can have arelated key that can be utilized to locate information about parents orchildren of the element in the original hierarchy, for example.

Referring now to FIG. 4, a system 400 for transforming streams andproviding subsequent access to stream data is displayed. A compositestream transformation component 102 is provided having a source streaminput component 402 that takes a stream as input, a streamtransformation component 404 that transforms the input stream to atleast one disparate format, a store stream output component 406 thatoutputs the disparately formatted streams to one or more outputs orstores, and a data access component 408 that facilitates subsequentaccess to the data. In one embodiment, the source stream input component402 can pull a source data stream and stop upon reaching the start orend of a point of interest. The transformation component 404 cantransform the stream, or a portion thereof (e.g. relevant fields) to adisparate format and/or architecture and the store stream outputcomponent 406 can store the transformed streams. Subsequently, the dataaccess component 408 can field requests for data and return appropriateresults.

The source stream input component 402 can take a stream as input bysubstantially any method including request/response, subscriptionrequest (resulting in notification of new data, for example), pulling aspecified portion, etc. In one embodiment, the source stream inputcomponent 402 pulls the stream as a result of a request from one or morecomponents or applications, for example. Additionally, the source streaminput component 402 can pull for data, for example, based in part on apull request to a disparate data store that houses data output by thestore data output component 406, for example. In another embodiment, thesource stream input component 402 can stop the stream, for example, uponreaching a desired item (an item of interest, for example). Uponstopping the stream, for instance, the item of interest can be read infull (e.g. until an element indicating the end of the item of interest)or incrementally until an end point is reached.

The stream transformation component 404 can consume the stream data; thedata can be related to one or more points of interest, for example. Theconsumed data, or a portion thereof, can be transformed to one or moredisparate types for input into a disparate structure, for example. Thedisparate structure can be stored such to allow virtually endlessstorage, for example, thus allowing the stream to be accessible whilenot increasingly utilizing memory of a component, such as the compositestream transformation component 102. In fact, once the stream is inputand transformed, the stream can be output (by the store stream outputcomponent 406, for example) and then disposed of. Thus, the memory ofthe composite stream transformation component 102, or a system,component, or application associated therewith, can be finite sinceneither the input stream nor the output stream remains in memory. Inthis regard, the stream transformation component 404 can be considered areader, in effect, as well like the source stream input component 402.While the source stream input component 402 can consume an availableportion of the stream (which can be one or more elements), the streamtransformation component 404 can advance the consumed stream an elementat a time, transforming selected portions for output thereof to thestore stream output component 406. The dual or multiple reading can havea domino effect, for example, where a component or application can causethe stream transformation component 404 to read an element (such as byaccessing the data stream store or store stream output component 406,for example) which can cause the source stream input component 402 toread a portion of the data input stream, for instance.

The store stream output component 406 can persist the transformedinstances of the stream to one or more disparate storage types/formatson one or more disparate storage devices, for example, to facilitatelong-term storage, redundant storage, and/or alternative storage of thedata stream, for example. The output format can be substantially anyoutput format, and a hierarchical structure of the data, if existent,can be maintained. Additionally, where the input data is relational, therelationships can be maintained in the transformation and output of thedata by the store stream output component 406 as well. For example,where the data is related by primary and/or foreign keys, the keyinformation can be use to provide a hierarchy to the data when stored ina hierarchical or nested format (such as XML), for example. The streamstore output component 406 can output the data in a number of streams,and the streams can correspond to one or more entities related to thestore as well. Conversely, for example, where the data is output to arelational database, streams can be open by the store stream outputcomponent 406 for each element of interest, for example. The stream canrelate to and insert data as a row within a table, for example. In thisregard, items of interest can be stored together in the table and keyscan be used to retain nesting and/or hierarchical format of the inputdata stream.

The data access component 408 can facilitate subsequent access of thedata stored in the data stream stores by the stream store outputcomponent 406, for example. In one embodiment, the data can be returnedin substantially the same format as it was in the input data stream, forexample. Thus, where the data in the input data stream was hierarchicalin nature, the hierarchy can be retained even where the data is outputto a flat format (such as a relational database or file-based format).One way to effectuate this while still providing efficienttransformation of the stream is to allow the stream transformationcomponent 404 to retrieve a complete row as a stream. This correlates toreceiving an entire stream of related elements in a single retrievalsuch that the order can be preserved but stream processing andtransformation can continue with respect to the stream. In XML, forexample, this can entail reading an entire point of interest tag (whichcan comprise other point of interest tags) before processing such toretain the hierarchy and order. In this regard, the point of intereststream can be processed together as a group and streaminput/transformation can continue. In one embodiment, the first point ofinterest reached in each case can be read as a single row ensuring thatthe hierarchy is maintained for the highest level items on down.Additionally, other schemes can be utilized as well, for example forhigher-level nodes that are not high in number in the stream but have anumber of children. In this regard, the children can be read as singlerows (along with embedded points of interest). It is to be appreciatedthat this can be configurable and/or machine-learning can be utilized todecide efficient methods of using this technology.

Additionally, the data can be flat in the input stream and going to ahierarchical structure (or another flat structure, for example). Wherethe data was flat in the input data stream and perhaps had a relationalaspect to it, the relationships between the data can be retained, forexample, if the data is stored in a hierarchical format (such as bynesting, for example). Similar methods can be used to effectuate thisconverse behavior as well. For this reason, when data access isrequested from the data access component 408, it can be returned insubstantially the same format as it originally came in as the structureof the data can be retained in other formats using other syntax, forinstance.

Now referring to FIG. 5, a system 500 for transforming an XML streaminto a relational database is illustrated as one example embodiment ofthe subject matter described herein. In this example, a data stream 502is provided in a nested format (such as XML), which is consumed by acomposite stream transformation component 102. The composite streamtransformation component 102 transforms the data and outputs it on oneor more streams into a data stream store 202, which stores the streamsin a relational database in this example to facilitate subsequentquerying thereof. The example provides database tables for items,similar items, location, and map info; the composite streamtransformation component 102 can identify stops in the XML streamrelating to the elements that correspond to the table, as well asidentify wanted information from the elements of interest. The followingpseudo-code can facilitate this functionality where the XML stream 502can be named “expo.xml.”

Sub Main( )  Dim conn As New SqlConnection(“server=...; database=...;”) Conn.Open( )  ‘Set up stream stops  Dim channel = NewXDataElement(“CHANNEL”)  Dim item = New XDataElement(“ITEM”)  Dim locs =New XDataElement(GetXmlNamespace(CLASS) + “LOC”)  ‘Set up mapping ofelements to tables  Dim itemsLoader = New TableLoader(“ITEMS”, AddressOf  CreateItemRow)  Dim similarItemsLoader = NewTableLoader(“SIMILAR_ITEMS”,   AddressOf CreateSimilarItemsRow) Item.Add(itemsLoader, similarItemsLoader)  Dim locationsLoader = newTableLoader(“LOCATIONS”, AddressOf   CreateLocationRow)  DimmapInfoLoader = New Tableloader(“MAP_INFO”, AddressOf   CreateMapInfo) locs.Add(locationsLoader, mapInfoLoader)  ‘load  Dim xBulkLoader =XSQLBulkLoader.LoadXMLStream(“expo.xml”,   conn, channel, item, locs) xBulkLoader.Execute( ) End Sub( ) Function CreateItemRow(ByVal item AsXElement) As Object( ) Return  New Object( ) {item.<CLASS:LID>.Value,item.<TITLE>.Value,  item.<LINK>.value, item.<DESC>.Value} End FunctionFunction CreateSimilarItemsRow(ByVal item As XElement) As  Object( ) ( )Return (From similarItem In item.<CLASS:SIMLIST>.-   <CLASS:LIST> SelectNew Object( ) {item.<CLASS:LID>.Value,    similarItem.<CLASS:LID>.Value,   similarItem.<CLASS:TITLE>.Value}).ToArray( ) End Function FunctionCreateLocationRow(ByVal location As XElement) As Object( )  Return NewObject( ) {location.Parent.<CLASS:LID>.Value, location.<GEO:LATLONG>.Value, location.<CLASS:ADDRESS>.-  Value} EndFunction Function CreateMapInfo(ByVal location As XElement) As Object( )( )  Return (From map In location.<CLASS:MAPLINKS>.-    <CLASS:MAPLINK>Select New Object( ) {location.-    <GEO:LATLONG>.Value,map.<CLASS:CO>.Value,    map.<CLASS:URL>.Value}).ToArray( ) End FunctionIn the foregoing pseudo-code, a database connection is instantiated forstreaming the XML thereto. Subsequently, stream stops are setup asdescribed herein as points or elements of interest. The “CHANNEL,”“ITEM,” and “CLASS:LOC” tags are specified as stop points. It is atthese points the input stream can stop and the composite streamtransformation component 102 can process the stream into one or moreoutput streams. This can act as a trigger, for example, and call thefunction pointers as shown in the code, CreateItemRow,CreateSimilarItemsRow, CreateLocationRow, and CreateMapInfo. A pluralityof TableLoaders are created that call the function pointers for therespective output streams (or database tables, in this case) upontrigger from the XML stream consumption. Thus, for example, when “ITEM”is consumed (the start tag or the end tag, for example), the TableLoaderis triggered and calls the CreateItemRow and CreateSimilarItemsRowfunctions, which return database rows using the data in the XML stream.

The following pseudo-code can be used to implement the portion of thecomposite stream transformation component 102 that reads the XML stream.

using System; using System.Collections; usingSystem.Collections.Generic; using System.IO; using System.Linq; usingSystem.Xml; using System.Xml.Linq; namespace System.Xml.Linq {  classStreamLoader  {   class StreamIterator : IEnumerable<XElement>   {   StreamLoader loader;    int index;    internal XElement source;   public StreamIterator(StreamLoader loader, int index, XElementsource)    {     this.loader = loader;     this.index = index;    this.source = source;    }    public IEnumerator<XElement>GetEnumerator( )    {     if (loader.index != index − 1 || (index − 1 >=0 && loader.iterators[index − 1] != null && loader.iterators[index −1].source != source)) yield break;     int depth = loader.baseDepth +index + 1;     XName name = loader.streamNames[index];     XNamestreamName = loader.streamNames.Length > index + 1 ? loader.-streamNames[index + 1] : null;     if (loader.SkipContentUntil(depth,name))     {      loader.iterators[index] = this;      loader.index =index;      do      {       source = new XElement(name);      loader.ReadElementUntil(source, streamName);       if (streamName!= null)       {        source.AddAnnotation(loader);       }      yield return source;       if (loader.iterators[index] != this)yield break;       if (loader.index != index)       {        for (int i= index + 1; i <= loader.index; i++)        {        loader.iterators[i] = null;        }        loader.index =index;       }      } while (loader.SkipContentUntil(depth, name));     loader.iterators[index] = null;      loader.index = index − 1;    }    }    IEnumerator IEnumerable.GetEnumerator( )    {     return(IEnumerator)GetEnumerator( );    }   }   XmlReader reader;   intbaseDepth;   XName[ ] streamNames;   StreamIterator[ ] iterators;   intindex;    public StreamLoader(XmlReader reader, XName[ ] streamNames)   {     this.reader = reader;     this.baseDepth = reader.Depth;    this.streamNames = streamNames;     this.iterators = newStreamIterator[streamNames.Length];     this.index = −1;    }    publicIEnumerable<XElement> Stream(XElement e)    {     if (index >= 0 &&iterators[index].source != e) return XElement.EmptySequence;     returnnew StreamIterator(this, index + 1, e);    }    public voidReadElementUntil(XElement source, XName match)    {     if(reader.MoveToFirstAttribute( ))     {      do      {       XNamespacens = reader.Prefix.Length == 0 ? XNamespace.Blank :XNamespace.Get(reader.NamespaceURI);       source.Add(newXAttribute(ns.GetName(reader.LocalName), reader.Value));      } while(reader.MoveToNextAttribute( ));      reader.MoveToElement( );     }    if (!reader.IsEmptyElement)     {      reader.Read( );      if(match != null)      {       if (ReadPrologUntil(source, match)) return;     }      else      {       ReadContent(source);      }     }    reader.Read( );    }    void ReadContent(XElement source)    { if(reader.NodeType != XmlNodeType.EndElement)     {      do      {      source.Add(XNode.ReadFrom(reader));      } while (reader.NodeType!= XmlNodeType.EndElement);     }     else     {     source.Add(string.Empty);     }    }    boolReadPrologUntil(XElement source, XName match)    {     if(reader.ReadState != ReadState.Interactive) throw newInvalidOperationException(“The reader state should be Interactive.”);    do     {      switch (reader.NodeType)      {       caseXmlNodeType.Element:        XName name =XNamespace.Get(reader.NamespaceURI).GetName(reader.LocalName);        if(name == match) return true;        XElement e = new XElement(name);       if (reader.MoveToFirstAttribute( ))        {         do         {         XNamespace ns = reader.Prefix.Length == 0 ? XNamespace.Blank :XNamespace.Get(reader.NamespaceURI);          e.Add(newXAttribute(ns.GetName(reader.LocalName), reader.Value));         } while(reader.MoveToNextAttribute( ));         reader.MoveToElement( );       }        source.Add(e);        if (!reader.IsEmptyElement)       {         reader.Read( );         ReadContent(e);        }       break;       case XmlNodeType.EndElement:        return false;      case XmlNodeType.Text:       caseXmlNodeType.SignificantWhitespace:       case XmlNodeType.Whitespace:      case XmlNodeType.CDATA:       case XmlNodeType.Comment:       caseXmlNodeType.ProcessingInstruction:       case XmlNodeType.DocumentType:       break;       case XmlNodeType.EntityReference:       reader.ResolveEntity( );        break;       caseXmlNodeType.EndEntity:        break;       default:        throw newInvalidOperationException(String.Format(“The reader should not be on anode of type ‘{0}’.”, reader.NodeType));      }     } while(reader.Read( ));     return false;    }    bool SkipContentUntil(intdepth, XName match)    {     if (reader.ReadState !=ReadState.Interactive) return false;     do     {      int d =reader.Depth;      if (d == depth)      {       if (reader.NodeType ==XmlNodeType.Element)        {         XName name =  XNamespace.Get(reader.NamespaceURI).GetName(reader.LocalName);        if (name == match) return true;        }       }       else if(d < depth)       {        break;       }      } while (reader.Read( ));     return false;     }    }    public static class StreamExtensions   {     public static XElement LoadStream(string uri, XName rootName,  params XName[ ] streamNames)     {      returnLoadStream(XmlReader.Create(uri,   GetXmlReaderSettings( )), rootName,streamNames);     }     public static XElement LoadStream(TextReadertextReader, XName   rootName, params XName[ ] streamNames)     {     return LoadStream(XmlReader.Create(textReader,  GetXmlReaderSettings( )), rootName, streamNames);     }     publicstatic XElement LoadStream(XmlReader reader, XName   rootName, paramsXName[ ] streamNames)     {     XElement source = newXElement(rootName);      StreamLoader loader = new StreamLoader(reader,streamNames);      XName streamName = streamNames.Length > 0 ?streamNames[0] :   null;      loader.ReadElementUntil(source,streamName);      if (streamName != null)      {      source.AddAnnotation(loader);      }      return source;     }    public static IEnumerable<XElement> Stream(this XElement source)    {      StreamLoader loader = source.Annotation<StreamLoader>( );     return loader.Stream(source);     }     public staticIEnumerable<XElement> Stream(this   IEnumerable<XElement> source)     {     if (source == null) throw new ArgumentNullException(“source”);     return Enumerable.SelectMany(source, e => e.Stream( ));     }  static XmlReaderSettings GetXmlReaderSettings( )   {   XmlReaderSettings readerSettings = new XmlReaderSettings( );   readerSettings.ProhibitDtd = false;   readerSettings.IgnoreWhitespace = true;    return readerSettings;   } } }The foregoing code can receive a list of elements at which to stop whilestill progressing in the stream; the stop elements can be related to thepoints of interest, for example. The materialized XML data for a givennode can be returned at each level, except that of the next stop elementin the stream, for example. The following pseudo-code can be used toimplement a data reader that transforms the XML into one or morerelational database streams. In this example, the reader is called anXDataReader.

Imports System.Xml.Linq Imports System ImportsSystem.Collections.Generic Imports System.Data.SqlClient ImportsSystem.Data Imports System.Xml.Linq.StreamExtensions NamespaceSystem.Xml.Linq   Public Class XDataReader     Implements IDataReader#Region “Class members”     Private xStream As IEnumerator(Of XElement)    Private currentContext As XDataElement = Nothing     PrivatecurrentElement As XElement     Private currentRecord As Object( )    Private currentTableLoader As TableLoader = Nothing     PrivatexRowReader As New XRowReader( )     Private dElements As IEnumerable(OfXDataElement)     Private ancesotrsStack As Stack(Of XElement)    Private tableLoaders As Dictionary(Of TableLoader, SqlBulkCopy)    Private isFirst As Boolean = True     Private parentElement AsXElement #End Region     Sub New(ByVal elements As IEnumerable(OfXElement), ByVal loaders As Dictionary(Of TableLoader, SqlBulkCopy),ByVal dataElements As IEnumerable(Of XDataElement), ByVal ancestors AsStack(Of XElement), ByVal streamContext As XElement)       tableLoaders= loaders       xStream = elements.GetEnumerator( )       currentContext= dataElements(0)       dElements = dataElements.Skip(1)      ancesotrsStack = ancestors       parentElement = streamContext    End Sub     Public Sub Load( )       currentTableLoader =currentContext.TableLoaders(0)       If xStream.MoveNext( ) = True Then        currentElement = xStream.Current         If currentTableLoaderIsNot Nothing AndAlso currentContext.IsLeaf = True Then          parentElement.Add(currentElement)           currentRecord =currentTableLoader.TableRowCreator(currentElement)          currentElement.Remove( )tableLoaders(currentTableLoader).WriteToServer(Me)         Else          While Me.Read( )           End While         End If       EndIf     End Sub     Public Function Read( ) As Boolean ImplementsData.IDataReader.Read       If isFirst = True Then         isFirst =False         parentElement.Add(currentElement)         IfcurrentTableLoader IsNot Nothing Then           LoadSelf( )         EndIf         If dElements(0) IsNot Nothing Then           LoadChildren( )        End If         currentElement.Remove( )         Return True      End If       If xStream.MoveNext( ) = True Then        currentElement = xStream.Current        parentElement.Add(currentElement)         If currentTableLoaderIsNot Nothing Then           LoadSelf( )         End If         IfdElements(0) IsNot Nothing Then           LoadChildren( )         End If        currentElement.Remove( )         Return True       Else        Return False       End If     End Function     Private SubLoadSelf( )       currentRecord =currentTableLoader.TableRowCreator(currentElement)       IfcurrentContext.IsLeaf = False Then         xRowReader.Add(currentRecord)tableLoaders(currentTableLoader).WriteToServer(xRowReader)       End If      ‘handle the case of more tables needs to be loaded from the sameelement       For Each tbl In currentContext.TableLoaders.Skip(1)        If tbl.TableRowsCreator IsNot Nothing Then           For Eachrecord In tbl.TableRowsCreator(currentElement)            xRowReader.Add(record)            tableLoaders(tbl).WriteToServer(xRowReader)           Next        Else           Dim record = tbl.TableRowCreator(currentElement)          xRowReader.Add(record)          tableLoaders(tbl).WriteToServer(xRowReader)         End If      Next     End Sub     Private Sub LoadChildren( )      ancesotrsStack.Push(currentElement)       Dim xdReader = NewXDataReader(currentElement.Stream( ), tableLoaders, dElements,ancesotrsStack, currentElement)       xdReader.Load( )      ancesotrsStack.Pop( )     End Sub #Region “Boiler codeimplementation of IDataReader”   End Class End NamespaceThis function can utilize the TableLoaders from the first pseudo-codesample to create rows in the appropriate database tables according tothe data in the XML stream. In this example, the reader implements apull model where a reader of the output stream (e.g. of the XDataReader)can request a value to be written, which causes the reader of the XMLstream to ask for the next value in the stream. Thus, there can be twoways to read a stream. One way is to read the entire available stream,and another way to read the stream is by reading one item or element ata time, for example. In this embodiment, the XML reader can read theentire available stream and then the XDataReader can read the stream oneelement at a time to store the relevant streams, for instance.

In this regard, a stream comprising a row can be formulated of relatedelements of the XML stream (such as an item of interest and itschildren). Thus, a pulling a stream can return a single row with therelated XML elements. This can facilitate efficiently parsing the XMLstream and retaining its hierarchical order by allowing consumption ofthe stream while processing each row; since the row is kept together,the hierarchy and order can be maintained. As described above, multipleschemes can be implemented to decide where this functionality should beimplemented. For example, the first point of interest in each read canbe read as a row to ensure all children hierarchy and order areretained. However, where the first point of interest is large and thetransformation must wait a while before receiving the end tag, a childpoint of interest can be used instead. It is to be appreciated that oneor more points of interest can be read as a row along with the embeddedpoints of interest related thereto. Additionally, inference can be usedto determine which points of interest must be read as a single row inthis regard. This can be coded according to the following pseudo-codeimplemented within the XDataRow class represented in some of theforegoing pseudo-code, for example.

Public Class XRowReader   Implements IDataReader   Private currentRow AsObject( )   Private hasBeenRead As Boolean = False   Public SubAdd(ByVal tableRow As Object( ))     currentRow = tableRow    hasBeenRead = False   End Sub   Public Function Read( ) As BooleanImplements System.Data.IDataReader.Read     If hasBeenRead = False Then      hasBeenRead = True       Return True     End If     Return False  End Function #Region “Boiler code implementation of IDataReader” EndClassAdditionally, as shown in previous pseudo-code, an XDataElement class isprovided to implement stopping the stream and returning the stoppedelement, as well as to define the shape of the relational stream outputin this example. The XDataElement can be resembled by the followingpseudo-code.

Public Class XDataElement   Dim xname As XName   Dim tblLoaders AsTableLoader( ) = { }   Dim isLeafElement As Boolean = False   Public SubNew(ByVal elementName As XName)    xname = elementName   End Sub  Public Sub Add(ByVal ParamArray tableLoaders As TableLoader( ))    IftblLoaders.Length = 0 Then     tblLoaders = tableLoaders    Else     Dimlength = tblLoaders.Length     ReDim Preserve tblLoaders(length +tableLoaders.Length)     For i = 0 To tableLoaders.Length − 1     tblLoaders(length + i) = tableLoaders(i)     Next    End If   EndSub   Public ReadOnly Property Name( ) As XName    Get     Return xname   End Get   End Property   Public ReadOnly Property TableLoaders( ) AsIEnumerable(Of TableLoader)    Get     Return tblLoaders    End Get  End Property   Friend Property IsLeaf( ) As Boolean    Get     ReturnisLeafElement    End Get    Set(Byval value As Boolean)    isLeafElement = value    End Set   End Property End ClassThis class leverages the TableLoader class to store and returnTableLoaders associated with the transformation process. The TableLoaderclass can associate a table name with a function that returns an objectarray that represents a row to be output to the relational stream. Inother embodiments, a similar loader method can be provided thatassociates callback functions with locations for the output streams suchthat when the point of interest is hit, the data can be transformed bythe callback and transmitted to the output stream (and to the finaldestination, for example). The TableLoader class in this example can beimplemented by the following pseudo-code (and/or coded as a hash table),for example.

Public Class TableLoader  Dim tblName As String  Dim rowCreator AsFunc(Of XElement, Object( )) = Nothing  Dim rowsCreator As Func(OfXElement, Object( )( )) = Nothing  Public Sub New(ByVal tableName AsString, ByVal tableRowCreator As Func(Of XElement, Object( )))   tblName= tableName   rowCreator = tableRowCreator  End Sub  Public SubNew(ByVal tableName As String, ByVal tableRowsCreator As Func(OfXElement, Object( )( )))   tblName = tableName   rowsCreator =tableRowsCreator  End Sub  Public ReadOnly Property TableName( ) AsString   Get    Return tblName   End Get  End Property  Public ReadOnlyProperty TableRowCreator( ) As Func(Of XElement, Object( ))   Get   Return rowCreator   End Get  End Property  Public ReadOnly PropertyTableRowsCreator( ) As Func(Of XElement, Object( )( ))   Get    ReturnrowsCreator   End Get  End Property End ClassAdditionally, a bulk loader class can be implemented to pull thecomponents together to achieve the desired behavior in this example. Thebulk loader, shown in the previous pseudo-code, starts the streamreading, transforming and output process. A sample bulk loader can beimplemented like the following in conjunction with the other pseudo-codeshown supra.

Imports System.Xml.Linq Imports System ImportsSystem.Collections.Generic Imports System.Data.SqlClient ImportsSystem.Data Imports System.Xml.Linq.StreamExtensions Public ClassXSQLBulkLoader  Private xeReader As XElement  Private dbConnection AsSqlConnection  Private dtElements As XDataElement( )  Private loaders AsNew Dictionary(Of TableLoader, SqlBulkCopy) #Region “CreateSQLBulkLoader methods”  Public Shared Function LoadXmlStream(ByVal uriAs String, ByVal connection As SqlConnection, ByVal ParamArraydataElements( ) As XDataElement) As XSQLBulkLoader   Dim path = FromdataElement In dataElements Select dataElement.Name   Return NewXSQLBulkLoader(StreamExtensions.LoadStream(uri, path(0),path.Skip(1).ToArray( )), connection, dataElements)  End Function #EndRegion  Private Sub New(ByVal inputXElementReader As XElement, ByValconnection As SqlConnection, ByVal ParamArray dataElements AsXDataElement( ))   If connection Is Nothing OrElse connection.State <>ConnectionState.Open Then    Throw New ArgumentException(“The connectionto the database must be opened”)   End If   dbConnection = connection  xeReader = inputXElementReader   dtElements = dataElements  dtElements(dataElements.Count − 1).IsLeaf = True   For Each ele IndtElements    For Each tblLoader In ele.TableLoaders     Dim c = NewSqlConnection(dbConnection.ConnectionString)     c.Open( )     DimsqlBulkloader As New SqlBulkCopy(c)    sqlBulkloader.DestinationTableName = tblLoader.TableName    loaders.Add(tblLoader, sqlBulkloader)    Next   Next  End Sub Public Sub Execute( )   Dim stack As New Stack(Of XElement)  stack.Push(xeReader)   Dim xdReader = New XDataReader(xeReader.Stream(), loaders, dtElements.Skip(1), stack, xeReader)   xdReader.Load( )  EndSub End ClassIt is to be appreciated that the subject matter as disclosed herein isnot limited to the example and pseudo-code provided above. This is butone of many possible implementations in accordance with the componentsand methods described above as well as those described infra.

The aforementioned systems, architectures and the like have beendescribed with respect to interaction between several components. Itshould be appreciated that such systems and components can include thosecomponents or sub-components specified therein, some of the specifiedcomponents or sub-components, and/or additional components.Sub-components could also be implemented as components communicativelycoupled to other components rather than included within parentcomponents. Further yet, one or more components and/or sub-componentsmay be combined into a single component to provide aggregatefunctionality. Communication between systems, components and/orsub-components can be accomplished in accordance with either a pushand/or pull model. The components may also interact with one or moreother components not specifically described herein for the sake ofbrevity, but known by those of skill in the art.

Furthermore, as will be appreciated, various portions of the disclosedsystems and methods may include or consist of artificial intelligence,machine learning, or knowledge or rule based components, sub-components,processes, means, methodologies, or mechanisms (e.g., support vectormachines, neural networks, expert systems, Bayesian belief networks,fuzzy logic, data fusion engines, classifiers . . . ). Such components,inter alia, can automate certain mechanisms or processes performedthereby to make portions of the systems and methods more adaptive aswell as efficient and intelligent, for instance by inferring actionsbased on contextual information. By way of example and not limitation,such mechanism can be employed with respect to generation ofmaterialized views and the like.

In view of the exemplary systems described supra, methodologies that maybe implemented in accordance with the disclosed subject matter will bebetter appreciated with reference to the flow charts of FIGS. 6-8. Whilefor purposes of simplicity of explanation, the methodologies are shownand described as a series of blocks, it is to be understood andappreciated that the claimed subject matter is not limited by the orderof the blocks, as some blocks may occur in different orders and/orconcurrently with other blocks from what is depicted and describedherein. Moreover, not all illustrated blocks may be required toimplement the methodologies described hereinafter.

FIG. 6 illustrates a methodology 600 for transforming a data stream fromone format and storing it in another format (or the same format in adifferent location, for example). As described, this can have manyembodiments and utilities such as to transfer a stream to a persistentstorage that can be managed outside of the stream processing. Thisallows access to virtually limitless portions of a stream (e.g. as muchas the persistent storage can hold) while not utilizing the main memoryof a stream device or a consumption device for example. Another aim, inthis regard, is to maintain structure, or be able to restructure thedata according to the original structure, upon storage. Thus, in oneembodiment, the data can be hierarchical and/or nested data stored in apersistent storage, such as a relational database (local and/ordistributed) to facilitate efficient querying. In this regard, asdescribed above, structure can be kept (or stored) using a series ofkeys to associated the hierarchical data in its stored relational form.

At 602, the data stream is pulled in a first format. As described, thiscan be the result of a pull request from another entity (such as for aportion, element, or entirety of the stream). The data stream can besubstantially any stream in substantially any format. As described, thestream can be for many purposes comprising many different data contentsor mixtures thereof. The data can be pulled as it comes in via anotification event, for example, or request/response behavior. At 604,the pulled data stream is read until a point of interest is consumed. Ifno such point exists, the data can be continually pulled and read untilsuch point is reached. The point of interest can relate to an element orattribute of the data string, for example, and can act as a trigger toperform some function on the stream. For instance, the consumption of apoint of interest can cause a portion of the stream to be transformedinto a different structure at 606.

Such transformation can keep a structure of the first format upontransformation, such as a relational or hierarchical structure (whateverstructure the first format can have). This can be done in a number ofways depending on the original and stored structures of the data. In oneembodiment, the data can be XML formatted data, for example, having aplurality of start and end tags indicating elements, where the elementscan be nested to form a hierarchical structure. This structure canrepresent a relationship between the elements, for example. In thisembodiment, the stream can be transformed to a different structure (orthe same structure on a different storage medium, for example). Storingthe XML in a relational database, however, can retain structure usingkeys (such as primary and foreign keys) to relate the data to oneanother such that subsequent retrieval can form the data insubstantially the same format as received in the original stream(preserving order and hierarchy, for example). Additionally, theconverse storage of relational data to XML, for example, can utilizehierarchical structure to preserve key usage. It is to be appreciatedthat there are many other examples and methods for different formats;for example, taking an XML stream to a flat text file, other fields orindicators (unique identifiers) can be used to relate data to itshierarchical structure. At 608, the data is output in the second format.As mentioned, this can be to utilize a persistent storage, for example,so long as the stream is consumed and output to a different formatand/or location. The output can also utilize one or more streams intoone or more sources for redundant, multiple, and/or alternative storageschemes.

FIG. 7 shows a methodology 700 for setting up a data streamtransformation architecture. This can be implemented by substantiallyany program application and/or device (such as a hardware device, etc.).Once up and running, the transformation architecture can operateaccording to one or more of the embodiments described herein. At 702,stop elements in the data stream are identified. The stop elements canrelate to portions of the stream, which when encountered cause an eventto occur (with respect to the stream, for example). The stop elementsallow for the stream to be parsed while pulling out only certain itemsfor further processing if desired. To this end, the entire stream neednot be stored, in some cases (unless desired, for example), rather thedata that is to be subsequently used can be pulled out. At 704, the stopelements are associated with callback functions that perform theforegoing behavior. The callback functions can be executed when the stopelements are hit and a specific element can have a specific callback.For example, in an XML to relational database embodiment, the callbackfunction can stop at certain XML elements and formulate a query stringto insert a portion of data related to the stop elements into adatabase, for example.

At 706, the callback functions are defined/specified to output data tothe stream. For example, the function can be implemented such that it isaccessible upon reaching a stop element. The function can be coded toperform a variety of output tasks related to the stream data, forexample. In one embodiment, the callback function can open an outputstream and write data to it according to a transformation scheme. Thus,in a file to database implementation, the database connection can beopened (unless globally opened already), the query string for insertioncan be formulated, and executed with the input stream data. Other taskscan be performed by the function as well; for example if deletion ofrows or creation of a new table is desired, for example. At 708, readingcan begin on the input stream and the architecture can be utilized toperform the described functionality.

FIG. 8 shows a methodology 800 for responding to a read request totransform a stream to a disparate format. As described, the stream canbe pulled from a device exposing the stream and then transmitted inportions of elements to facilitate efficient storage thereof whilemaintaining hierarchy and order. At 802, an available portion of theinput stream is pulled (e.g. from an exposing device). As described,this can be request/response and/or notification via subscription toeffectuate the read. The available portion of the stream can be inputinto a temporary buffer, for example, and at 804, the stream can beinterpreted in its native format. Thus, the stream can be read todetermine one or more elements according to the format; in one example,the stream can be XML and can be interpreted as a series of tags,elements, and attributes, and temporarily stored in memory to facilitateprogrammatic access thereof.

At 806, a request is received for one or more elements in the stream.The elements can be determined according to the interpreted stream; inthis regard, the stream can be requested one element at a time after theavailable portion is pulled. Additionally, the available portion can bepulled as a result of the request for the one or more elements. At 808,the requested elements are transmitted to the requestor. In oneembodiment, the requester can output the requested elements to an outputstream as further described herein, for example.

As used herein, the terms “component,” “system” and the like areintended to refer to a computer-related entity, either hardware, acombination of hardware and software, software, or software inexecution. For example, a component may be, but is not limited to being,a process running on a processor, a processor, an object, an instance,an executable, a thread of execution, a program, and/or a computer. Byway of illustration, both an application running on a computer and thecomputer can be a component. One or more components may reside within aprocess and/or thread of execution and a component may be localized onone computer and/or distributed between two or more computers.

The word “exemplary” is used herein to mean serving as an example,instance or illustration. Any aspect or design described herein as“exemplary” is not necessarily to be construed as preferred oradvantageous over other aspects or designs. Furthermore, examples areprovided solely for purposes of clarity and understanding and are notmeant to limit the subject innovation or relevant portion thereof in anymanner. It is to be appreciated that a myriad of additional or alternateexamples could have been presented, but have been omitted for purposesof brevity.

Furthermore, all or portions of the subject innovation may beimplemented as a method, apparatus or article of manufacture usingstandard programming and/or engineering techniques to produce software,firmware, hardware, or any combination thereof to control a computer toimplement the disclosed innovation. The term “article of manufacture” asused herein is intended to encompass a computer program accessible fromany computer-readable device or media. For example, computer readablemedia can include but are not limited to magnetic storage devices (e.g.,hard disk, floppy disk, magnetic strips . . . ), optical disks (e.g.,compact disk (CD), digital versatile disk (DVD) . . . ), smart cards,and flash memory devices (e.g., card, stick, key drive . . . ).Additionally it should be appreciated that a carrier wave can beemployed to carry computer-readable electronic data such as those usedin transmitting and receiving electronic mail or in accessing a networksuch as the Internet or a local area network (LAN). Of course, thoseskilled in the art will recognize many modifications may be made to thisconfiguration without departing from the scope or spirit of the claimedsubject matter.

In order to provide a context for the various aspects of the disclosedsubject matter, FIGS. 9 and 10 as well as the following discussion areintended to provide a brief, general description of a suitableenvironment in which the various aspects of the disclosed subject mattermay be implemented. While the subject matter has been described above inthe general context of computer-executable instructions of a programthat runs on one or more computers, those skilled in the art willrecognize that the subject innovation also may be implemented incombination with other program modules. Generally, program modulesinclude routines, programs, components, data structures, etc. thatperform particular tasks and/or implement particular abstract datatypes. Moreover, those skilled in the art will appreciate that thesystems/methods may be practiced with other computer systemconfigurations, including single-processor, multiprocessor or multi-coreprocessor computer systems, mini-computing devices, mainframe computers,as well as personal computers, hand-held computing devices (e.g.,personal digital assistant (PDA), phone, watch . . . ),microprocessor-based or programmable consumer or industrial electronics,and the like. The illustrated aspects may also be practiced indistributed computing environments where tasks are performed by remoteprocessing devices that are linked through a communications network.However, some, if not all aspects of the claimed subject matter can bepracticed on stand-alone computers. In a distributed computingenvironment, program modules may be located in both local and remotememory storage devices.

With reference to FIG. 9, an exemplary environment 900 for implementingvarious aspects disclosed herein includes a computer 912 (e.g., desktop,laptop, server, hand held, programmable consumer or industrialelectronics . . . ). The computer 912 includes a processing unit 914, asystem memory 916 and a system bus 918. The system bus 918 couplessystem components including, but not limited to, the system memory 916to the processing unit 914. The processing unit 914 can be any ofvarious available microprocessors. It is to be appreciated that dualmicroprocessors, multi-core and other multiprocessor architectures canbe employed as the processing unit 914.

The system memory 916 includes volatile and nonvolatile memory. Thebasic input/output system (BIOS), containing the basic routines totransfer information between elements within the computer 912, such asduring start-up, is stored in nonvolatile memory. By way ofillustration, and not limitation, nonvolatile memory can include readonly memory (ROM). Volatile memory includes random access memory (RAM),which can act as external cache memory to facilitate processing.

Computer 912 also includes removable/non-removable,volatile/non-volatile computer storage media. FIG. 9 illustrates, forexample, mass storage 924. Mass storage 924 includes, but is not limitedto, devices like a magnetic or optical disk drive, floppy disk drive,flash memory or memory stick. In addition, mass storage 924 can includestorage media separately or in combination with other storage media.

FIG. 9 provides software application(s) 928 that act as an intermediarybetween users and/or other computers and the basic computer resourcesdescribed in suitable operating environment 900. Such softwareapplication(s) 928 include one or both of system and applicationsoftware. System software can include an operating system, which can bestored on mass storage 924, that acts to control and allocate resourcesof the computer system 912. Application software takes advantage of themanagement of resources by system software through program modules anddata stored on either or both of system memory 916 and mass storage 924.

The computer 912 also includes one or more interface components 926 thatare communicatively coupled to the bus 918 and facilitate interactionwith the computer 912. By way of example, the interface component 926can be a port (e.g., serial, parallel, PCMCIA, USB, FireWire . . . ) oran interface card (e.g., sound, video, network . . . ) or the like. Theinterface component 926 can receive input and provide output (wired orwirelessly). For instance, input can be received from devices includingbut not limited to, a pointing device such as a mouse, trackball,stylus, touch pad, keyboard, microphone, joystick, game pad, satellitedish, scanner, camera, other computer and the like. Output can also besupplied by the computer 912 to output device(s) via interface component926. Output devices can include displays (e.g., CRT, LCD, plasma . . .), speakers, printers and other computers, among other things.

FIG. 10 is a schematic block diagram of a sample-computing environment1000 with which the subject innovation can interact. The system 1000includes one or more client(s) 1010. The client(s) 1010 can be hardwareand/or software (e.g., threads, processes, computing devices). Thesystem 1000 also includes one or more server(s) 1030. Thus, system 1000can correspond to a two-tier client server model or a multi-tier model(e.g., client, middle tier server, data server), amongst other models.The server(s) 1030 can also be hardware and/or software (e.g., threads,processes, computing devices). The servers 1030 can house threads toperform transformations by employing the aspects of the subjectinnovation, for example. One possible communication between a client1010 and a server 1030 may be in the form of a data packet transmittedbetween two or more computer processes.

The system 1000 includes a communication framework 1050 that can beemployed to facilitate communications between the client(s) 1010 and theserver(s) 1030. Here, the client(s) 1010 can correspond to programapplication components and the server(s) 1030 can provide thefunctionality of the interface and optionally the storage system, aspreviously described. The client(s) 1010 are operatively connected toone or more client data store(s) 1060 that can be employed to storeinformation local to the client(s) 1010. Similarly, the server(s) 1030are operatively connected to one or more server data store(s) 1040 thatcan be employed to store information local to the servers 1030.

By way of example, client(s) 1010 can read, transform, and output one ormore data streams to a persistent centralized storage. The client(s)1010 can temporarily consume a data stream and transform it into a morepersistent format while preserving order and hierarchy present in thestream. The client(s) 1010 can output the transformed stream across thecommunication framework 1050 to one or more server(s) 1030 and/or thedata store(s) 1040 associated therewith. Additionally, one or moreclient(s) 1010 can request access to the data stored in data store(s)1040 across communication framework 1050. The requested data can beretrieved from the data store(s) 1040 by one or more server(s) 1030 andtransformed back into the format of the original data stream.Subsequently, the restructured data can be sent back to the client(s)1010 from the server(s) 1030 in substantially the same format as it wasin the original data stream.

What has been described above includes examples of aspects of theclaimed subject matter. It is, of course, not possible to describe everyconceivable combination of components or methodologies for purposes ofdescribing the claimed subject matter, but one of ordinary skill in theart may recognize that many further combinations and permutations of thedisclosed subject matter are possible. Accordingly, the disclosedsubject matter is intended to embrace all such alterations,modifications and variations that fall within the spirit and scope ofthe appended claims. Furthermore, to the extent that the terms“includes,” “has” or “having” or variations in form thereof are used ineither the detailed description or the claims, such terms are intendedto be inclusive in a manner similar to the term “comprising” as“comprising” is interpreted when employed as a transitional word in aclaim.

1. A system for transforming a stream into a disparate structure,comprising: a composite stream transformation component that transformsa portion of a data stream into a disparately structured format whilemaintaining structure indicators for subsequent restructuring of atleast a subset of the portion of the data stream; and a data streamstore that stores the transformed data stream and allows subsequentaccess to the transformed data stream.
 2. The system of claim 1, thedata stream is hierarchically formatted and the composite streamtransformation component transforms the portion of the stream into aflat format.
 3. The system of claim 1, the disparately structured formatis a relational database format and primary keys facilitate themaintaining structure indicators.
 4. The system of claim 3, thecomposite stream transformation component restructures the subset of theportion of the data stream by associating elements of the subset into ahierarchy according to the primary keys.
 5. The system of claim 1,further comprising a data access component that responds to one or morerequests for the subset of the portion of the data stream.
 6. The systemof claim 1, the composite stream transformation component pulls the datastream from a component exposing the stream.
 7. The system of claim 6,the composite stream transformation component can pull an availableportion of the data stream from the component exposing the stream andinterpret the stream one element at a time until consuming a stopelement.
 8. The system of claim 7, the composite stream transformationcomponent executes a callback function based on the stop element, thecallback function transforms the subset of the portion of the datastream into the disparately structured format and outputs thedisparately structured subset to the data stream store.
 9. The system ofclaim 1, the composite stream transformation component uses inference todefine at least one transformation function that transforms the subsetof the portion of the data stream.
 10. A method for transforming ahierarchical stream into a flat structure, comprising: consuming ahierarchically formatted stream comprised of a plurality of nestedelements; transforming a portion of the stream into a flat structurewhile maintaining hierarchical indicia; and storing the transformedportion of the stream in a persistent data store according to the flatstructure.
 11. The method of claim 10, further comprising facilitatingaccess to the portion of the stream by restructuring the stream inhierarchical format utilizing the hierarchical indicia.
 12. The methodof claim 10, further comprising associating a stop element related tothe stream with a function for performing an activity on the stream. 13.The method of claim 12, the stop element relates to an identifier in thestring that triggers the function when the stop element is consumed. 14.The method of claim 12, the function defines procedures for thetransforming a portion of the stream and storing the transformed portionof the stream.
 15. The method of claim 10, further comprising requestinga stream of one element from the consumed stream to maintain thehierarchical format and an order of the stream when transforming theportion of the stream and storing the transformed portion of the stream.16. The method of claim 15, further comprising asynchronously consumingsubsequent portions of the stream upon receiving the stream of oneelement.
 17. The method of claim 10, the stream is consumed based atleast in part on a request for data made to the persistent data store.18. The method of claim 10, the stream is structured in an extensiblemarkup language (XML) format and is transformed into a relationaldatabase structure.
 19. A system for reshaping a portion of a streaminto a disparate structure, comprising: means for consuming a nesteddata input stream; means for reshaping the data input stream into adisparate flat structure while retaining aspects corresponding to theoriginal nested structure; means for pushing the reshaped data stream toan output stream for storage thereof.
 20. The system of claim 19,further comprising means for facilitating access to requests for thereshaped data, the reshaped data is shaped into the original nestedstructure using the retained aspects.